Analysis of data from a mass spectrometer

ABSTRACT

A programmed computer analyzes data from a mass spectrometer. A spectrum corresponding to an unknown sample is perturbed in various ways, and each perturbed spectrum is compared with the spectrum of a known or reference substance. The perturbed spectrum having the highest correlation with the known spectrum, and which is also physically plausible, is considered to be the best fit. The method indicates in what specific ways the unknown sample differs from, or is similar to, the known substance.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This is a division of U.S. patent application Ser. No. 10/856,736, filedMay 28, 2004 now U.S. Pat. No. 7,049,581, which claims the priority ofU.S. Provisional Patent Application Ser. No. 60/475,227, filed May 30,2003.

BACKGROUND OF THE INVENTION

This invention relates to the field of mass spectrometry, and provides amethod and apparatus for analyzing data obtained from a massspectrometer.

Mass spectrometers have long been used for performing qualitativeanalysis of substances. A mass spectrometer can essentially reduce atest sample to a set of ionic components, and displays the mass, andrelative abundance, of each such component. The mass spectrometerproduces an output that can be represented as a graph showing the massof each component (for example, on the horizontal axis) and theintensity, or relative abundance, of each component (for example, on thevertical axis). The graph generated by a mass spectrometer is called a“spectrum”.

Examples of the use of mass spectrometry in the field of biologicalscience are given in U.S. Pat. Nos. 6,017,693 and 5,538,897, thedisclosures of which are incorporated by reference herein.

A major problem in the use of a mass spectrometer is in the analysis ofthe spectrum generated by the device. Typically, an unknown substance isto be evaluated and compared with the spectrum of a known substance. Asimple visual comparison of the spectrum of the unknown substance with aknown spectrum is often insufficient and unproductive, as the points ofsimilarity between the spectra are often not apparent to the humanobserver.

Even numerical methods of comparison of spectra, known in the prior art,have been unsatisfactory. It has been known to calculate correlationsbetween spectra, but such calculations have been cumbersome andimpractical.

The present invention provides a computer-based method of analyzingspectra from a mass spectrometer. The method of the present inventionenables the user to obtain information about the spectrum of a testsample, even where such information is not intuitively obvious orreadily observable.

SUMMARY OF THE INVENTION

The present invention derives inferences concerning the composition ofan unknown sample, by comparing each of a set of perturbed spectra witha spectrum corresponding to a reference substance. The spectrum of theunknown sample is perturbed, in various ways, by introducing a shift ofone or more ionic components in the spectrum. The shifts introduced maybe derived by “brute force”, such as by using all available integers ordecimals, or they may be chosen according to experimental datadescribing known shifts caused by the presence of certain substances.Each of the perturbed spectra are then cross-correlated with thereference spectrum, and the perturbed spectrum having the highestcorrelation, and representing a physically plausible orapplication-relevant result, is deemed the “best” fit.

The perturbed spectrum that is considered the best fit can be used todraw inferences about how the unknown sample differs from, or how it issimilar to, known or reference compounds. In particular, the method ofthe invention makes it relatively easy to infer the presence of specificions in the unknown sample, based on the above-mentioned differencesfrom, or similarities to, known or reference samples.

The above-described method is preferably performed by a programmedcomputer that automates the correlation function. The inventiontherefore includes the method of performing the data analysis, and wellas the programmed computer, or equivalent device, that is used toperform the method.

The invention therefore has the primary object of providing an automatedmethod for analyzing data from a mass spectrometer.

The invention has the further object of providing a method and apparatusfor determining the specific ways in which an unknown substance differsfrom, and/or is related to, a known or reference substance.

The invention has the further object of providing a method ofqualitative analysis, which method uses experimental data concerning theeffect of the presence of specific substances, to draw inferences aboutthe composition of a material.

The invention has the further object of reducing the computation timerequired in the above-described method, by providing a technique fordiscarding ions and/or shifts that are not likely to yield usefulresults.

The reader skilled in the art will recognize other objects andadvantages of the present invention, from a reading of the followingbrief description of the drawings, the detailed description of theinvention, and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a graph representing the spectrum of a known substance,in a hypothetical example of the use of the present invention.

FIG. 2 provides a graph representing the spectrum of a hypotheticalunknown substance, in an example of the operation of the presentinvention, the graph indicating perturbations that make the spectrumcorrelate most closely with the spectrum of the known substance.

FIGS. 3 and 4 provide graphs which illustrate the application of thepresent invention to a series of mass spectra taken over a period oftime, these graphs showing the total intensity of each spectrum, foreach point in time.

FIGS. 5–7 provide graphs representing mass spectra taken at threespecific times within the range indicated in FIG. 3.

FIG. 8 provides a graph representing a mass spectrum of an unknownsample, taken at the time indicated by the vertical line in FIG. 4.

FIG. 9 provides a block diagram of the system of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention includes a method of processing data, obtainedfrom a mass spectrometer, to draw inferences about the composition of asample material. The invention is especially useful in analyzingmetabolites, impurities, and degradants that result from a givenchemical substance, such as a pharmaceutical, but is not limited to usein the latter fields. The general method will first be described in thefollowing overview, after which more specific details and examples willbe given.

Overview

A mass spectrometer separates various charged components in a substanceaccording to their mass to charge ratio. The mass spectrometer producesan output called a spectrum, which is essentially a graph whose datapoints correspond to ionized components in the sample. Analysis of thespectrum provides information about the molecular structure of thesample.

In this specification, the term “ion” is used in its broadest sense, toinclude any charged particle that can be detected by the massspectrometer. The term “ion” is sometimes also used to refer to a pointor vertical bar on a spectrum produced by a mass spectrometer, becausethe mass spectrometer separates and counts charged particles, and eachpoint or bar on the spectrum corresponds to a charged particle detectedby the instrument.

Each spectrum obtained from a mass spectrometer typically comprises agraph whose horizontal axis represents the masses of given ioniccomponents of a sample being tested, and whose vertical axis representsthe intensity of those ionic components, usually expressed in terms ofthe number of “counts” recorded by the mass spectrometer. In brief, thevertical axis indicates the relative abundance of a given component inthe sample. Each compound usually has its own unique spectrum, as viewedusing certain modes of mass spectrometric analysis, the spectrumcontaining a particular set of ions, and having a particular set ofhorizontal distances between ions, as viewed on the graph. Typically,similar ions correspond to the same substructure contained in thecompounds being compared, and differences between the ions in thesamples indicate structural differences. Compounds related structurallytend to produce spectra that are related, i.e. having similar ionsand/or similar differences between ions.

The spectrum produced by the mass spectrometer can be easily reduced tonumerical, and hence to digital, form. The spectrum can be stored, forexample, as a set of ordered pairs, each ordered pair representing theCartesian coordinates of a point on the spectrum. More generally, thehorizontal axis is typically subdivided into “bins” having a finitewidth, and the spectrum is stored in terms of the number of “counts”that are found within each bin.

The present invention uses the fact that the presence of certain ions ina sample will cause predictable changes in the appearance of thespectrum. Compounds that differ by one substructure, such as a hydroxylgroup, exhibit that difference in the spectra obtained from the massspectrometer. That is, the spectrum contains one ion which is “shifted”by an amount (measured in molecular mass units) corresponding to thathydroxyl group. More generally, the presence of a particular ion willcause a shift, to the left or to the right, of a given point on thespectrum. This shift is expressed in terms of the mass units used on thehorizontal axis. Thus, a sample which contains an ion having a mass of200 units, when chemically or biologically modified with a known moietyto cause a shift in mass of +16 units, can be expected, under normalcircumstances, to exhibit an ion located at a position corresponding toa mass of 216 units.

The present invention is typically used to compare a spectrum of anunknown substance with the spectrum of a known substance. However, theinvention can also be used where both spectra relate to unknownsubstances. That is, the invention can be used to determine how anunknown substance is similar to, or different from, some other unknownsubstance. Thus, in its broadest sense, the invention compares thespectrum of an unknown substance with the spectrum of a referencesubstance, and the reference substance may be known or unknown. In thisspecification, the term “known” will be used to describe the spectrum ofthe reference substance, but it is understood that this term includesthe case where the reference substance is itself unknown. Themethodology is exactly the same, whether the reference substance isknown or unknown.

In its most basic form, the method of the present invention starts witha spectrum of a known or reference substance, and a spectrum of thesubstance being analyzed. These spectra are stored in numerical form forease of manipulation. The method comprises repeatedly perturbing thespectrum of the unknown substance, by known shifts, to obtain a set ofdistinct, perturbed spectra, and correlating each such perturbedspectrum with the known spectrum. That is, one compares a large numberof different spectra, each one being obtained by perturbing the spectrumof the unknown substance, and numerically comparing each of such spectrawith the spectrum of the known substance. The spectrum having thegreatest correlation, while still being physically plausible andapplication-relevant, is deemed the best fit. The result is that one cancharacterize the unknown spectrum in terms of a known spectrum that isshifted by the presence of one or more ions.

In this specification, the terms “shift” and “perturbation” are usedinterchangeably.

The perturbations imposed on the spectrum of the unknown substance canbe derived in at least the following two ways. First, the user may startwith a list of known perturbations, corresponding to a set of known orexpected chemical modifications. The shifts caused by various chemicalmodifications, in the spectra produced by a mass spectrometer, can bepredicted from experimental observation, and these shifts are commonlyknown and available to the researcher. In using the list of knownperturbations, the method can be practiced by trying any or all of theseperturbations, to find a modified or perturbed spectrum that mostclosely correlates with the known spectrum. Secondly, the perturbationsapplied to the spectrum of the unknown substance may be unrelated toexperimental data. For example, one can perturb the points on thespectrum by every possible positive and negative value, up to apredetermined limit. The methodology is the same as before, except thatthe perturbed spectrum having the highest correlation to the givenspectrum may not correspond to a real substance. That is, by imposingarbitrary perturbations, the results obtained may not always bephysically meaningful, and additional analysis may be necessary toinsure a reasonable result.

EXAMPLE

This is a simplified example that shows the operation of the presentinvention. Suppose that an unknown spectrum contains two ions, havingmass values of 100 and 200, respectively. Suppose further that the userselects two perturbations, or shifts, having the values +10 and −20,respectively. Then every possible combination of ions and shifts are asshown in Table 1, below. Each entry in the table represents a perturbedspectrum, and each such perturbed spectrum will be compared,numerically, with the known spectrum.

TABLE 1 1) 100, 200 2) 100 + 10, 200 3) 100, 200 + 10 4) 100 + 10, 200 +10 5) 100 − 20, 200 6) 100, 200 − 20 7) 100 − 20, 200 − 20 8) 100 + 10,200 − 20 9) 100 − 20, 200 + 10

The comparison of each perturbed spectrum, with the known spectrum, canbe done by any known numerical method. A preferred method is to computea correlation coefficient between the spectra being compared. The caseof perfect correlation is defined as the correlation between a spectrumand itself. The degrees of correlation can be represented on anarbitrary scale, such as a range of 0–1, or 0–100, or some other range,the results of the correlation analysis being normalized to fall withinthe desired range, as is well known in the art. The present invention isnot limited to any specific method of performing the comparison.

The result is a list of correlation coefficients, each representing theresult of the comparison between one of the perturbed spectra and theknown spectrum.

This list of correlation coefficients can be used to “score” theperturbed spectra. Usually, the perturbed spectrum having the highestscore, i.e. having the highest correlation to the known spectrum, is theone that is chosen. However, application logic is included in theprogram, which logic is taken into account when the perturbed spectraare scored, such that the “best” perturbed spectrum is not necessarilythe one with the highest correlation coefficient.

As a result of the present method, one can characterize the unknownspectrum in terms of the known or reference spectrum, with the additionor removal of one or more shifts.

FIG. 1 provides an example of a hypothetical known spectrum. FIG. 2provides an example of a hypothetical spectrum of an unknown substance,indicating shifts to be applied to each data point. FIG. 2 illustratesthe choice of a “best” result, i.e. the perturbed spectrum of theunknown substance which has a high correlation with the known orreference spectrum, and which is application-relevant. These figures areexplained in more detail below.

Practical Details

The Choice of Perturbations

As explained above, the shifts or perturbations to be applied to thespectrum being analyzed can be taken from experimental data, or they canbe selected mechanically, using a “brute force” method wherein anexhaustive set of possible perturbations are all considered. Table 2shows various chemical modifications, and the shifts known to be causedby the presence of such chemical modifications, expressed in the sameunits as shown in the drawings.

TABLE 2 −56 Di-deethyl −32 Decarboxylation −30 Deoxy/Demethyl −28De-ethylation [O, N, S] −18 Des-fluoro −14 Des-methyl −14 Demethylation[O, N, S] −9 Des-Chloro/+CN −9 CN->OH −2 Dehydrogenation −1 Oxidativedeamination +2 Des-methyl/Hydroxylation +5 CN->CH₂OH +7Hydroxylation/CN->OH +14 Ketone +14 Methylation +16 N-oxide +16Sulfoxide +16 N-oxide +16 Hydroxylation +16 Epoxidation +30Hydroxy/Ketone +30 Methoxy +32 Dihydroxylation +34 Dihydro diol +42Acetylation +44 Des-Chloro/Bromination +80 Sulphate +96 Hydroxy/Sulphate+161 N-acetyl Cysteine +176 Glucuronide +192 Hydroxy/Glucuronide +305Glutathione

Any or all of the perturbations shown in Table 2, or other perturbationscorresponding to other chemical modifications, may be used. The computerwhich operates the method preferably includes a memory in which theseperturbations are stored, so that the computer can apply them asdescribed above. The computer may be programmed to allow the user toselect certain perturbations, or to apply automatically all possiblecombinations of perturbations, as illustrated in the Example, withoutguidance from the user.

It should be appreciated that the preferred method of perturbing thespectrum of the unknown substance is to subtract the known shift causedby a particular chemical modification. For example, if one wants toevaluate whether a particular substance has been hydroxylated, oneperturbs the ions produced from that substance by “removing” thehydroxyl, i.e. by subtracting 16 units from the position of thepertinent ion, because the table shows that the effect of hydroxylationis to shift the ion by +16. Similarly, for chemical modifications thatcause negative shifts, removal of those modifications is simulated byadding the absolute value of the pertinent values shown in the table.

Note that the decision whether to add or subtract a perturbation isimportant to the extent that it helps the user to obtain meaningfulresults. However, the computational methodology of the present inventionis unaffected by whether perturbations are added or subtracted. Ifperturbations are applied without regard to their physical meaning, theresults may not always be usable.

In an extreme case, the computer could simply apply perturbationscomprising, for example, all values between, say, −500 and +500, andgenerate spectra having all possible combinations of such perturbations.The perturbations could be integral or non-integral. The major limit onthe number of such possible perturbations is dictated by the computingresources available and the computation time required.

MS/MS Mode versus Normal Mode

In the description given above, no consideration was given to thevarious possible modes of operation of the mass spectrometer. Ingeneral, a mass spectrometer can operate in “normal” mode, which meansthat it simply provides a spectrum showing the relative concentration ofall ionizable components in the sample.

In many applications, only one ion is of interest. But if one limits theinquiry to ions having only one particular mass, the spectrum producedwill have only one data point, and the number of available perturbationsis small. It is therefore often convenient to use the “MS/MS” or“MS^(n)” mode, wherein the spectrum includes not only information aboutthe basic ion being studied, but also information about ions produced bycollision-induced dissociation.

In brief, the ion being studied can be caused to collide with gasmolecules, causing the ion to dissociate into substructural fragmentions. These fragments are themselves smaller ions which can be analyzedby the mass spectrometer. The signature of a particular compoundtherefore includes not only the ion of interest, but other ions that areproduced in collisions with gas molecules. When in MS/MS or MS^(n) mode,the mass spectrometer displays information concerning the original ion,as well as the collision-dissociated products. The collision productsare represented as points on a spectrum, and each can be perturbed inthe manner described above, to infer information about the structure ofthe substance being studied. Display of the collision products thereforerepresents a substance using a finer structure, making it possible toobtain more detailed information about the substance.

It should, however, be appreciated that the method of perturbing thepoints on the spectrum, and comparing each perturbed spectrum with aknown spectrum, is the same, in principle, regardless of whether themass spectrometer is operating in normal mode or in MS/MS mode.

Operation of the Method

In operating the method of the present invention, it is desirable firstto pre-process the data of each spectrum, so that the data arenormalized and in a suitable format for mathematical manipulation.Details of the pre-processing are given below. Also, it is helpful torank the importance of each ion in the unknown spectrum, and theimportance of each shift to be applied. If such a ranking can beaccomplished, the system can avoid the need to test ions and shiftswhich will not contribute to overall correlation increases with theknown or reference spectrum, as well as meaningless combinations of ionsand shifts. As is apparent from the example given above, the amount ofcomputation time increases exponentially as the number of ions andshifts increases, and even if a supercomputer is available, it may benecessary to take steps to limit the number of computations required.

A preferred method of ranking the ions and shifts is as follows. Eachion in the unknown spectrum which is not present in the known orreference spectrum, i.e. each ion which is unique to the unknownspectrum, and which is above a minimum relative intensity threshold thatis user-defined, is separately perturbed using all shifts defined in theprogram. This process is performed in the same manner as theperturbation approach described above, but using only a single ion atone time, and using all possible shifts. The result is a set of allpossible spectra containing the current unknown ion, as modified by allpossible shifts. In other words, one obtains a set of modified unknownspectra based on a single ion, and representing all possible shiftsapplied to that ion.

Each modified unknown spectrum in the above set is compared to the knownor reference spectrum using cross-correlation or any other method ofnumerical comparison. The highest correlation from each ion perturbationset is selected, whereby that correlation result represents that ionrelative to all other ions processed in the same manner. The individualion scores are ordered, highest to lowest, in terms of the correlationvalues obtained. Ions having a score below a user-defined minimum areremoved from further consideration. The highest n ions, in terms ofcorrelation, are selected, where n is determined by a user-definedmaximum number of ions to be allowed.

The result is a subset of all possible unknown ions whose rank and totalnumber meet the above-described ranking criteria. Only these ions areconsidered for further processing.

A similar process is used to rank each shift. Each shift is evaluatedindividually. For a given shift, all ions above a user-defined thresholdare perturbed by that shift, in all possible combinations. A set of allmodified unknown spectra, after applying the above combinations, isproduced and compared to the known or reference spectrum, as describedabove. Only the shifts chosen using the above approach are consideredfor further processing.

In short, the ranking method is similar to the basic method of analysis,except that only one ion, or only one shift, is considered at a time. Bydiscarding the ions or shifts that are unlikely to produce highcorrelations, considerable computing time can be avoided.

The following provides details about the pre-processing of data. A massspectrometer, operating in centroid mode, typically returnsmass/intensity data pairs which refer to a set of ionic components, asdescribed above. The term “profile mode” refers to acquisition ofcontinuum mass spectral data that are acquired at a constant samplinginterval or resolution. Typically, each detected ion in profile moderesembles a Gaussian shaped peak. Centroid mode includes converting eachprofile peak to a weighted average determination of peak center. Acentroided mass is typically represented by a vertical bar having atheoretically zero thickness, representing an exact reading of mass.

The mass portion of each data pair is usually a floating-point valuehaving integer and decimal parts. The decimal part is typicallycalculated with a precision of four or more significant figures. Theintensity portion is usually an integer. Since the precision of the massportion reported by the instrument is often higher than the actualaccuracy of the instrument, it is often desirable to reduce theprecision of the mass values to a level which is just below the typicalaccuracy of the instrument, or in the case of the present invention, auser-defined degree of precision corresponding to the desired number ofsignificant figures. In this way, any variability between two massmeasurements made on the same ionic component at different times, say123.1234 and 123.3234, is removed, thus giving exactly the same,less-precise mass measurement value for the same ionic components.

The reduction in mass measurement precision is also beneficial forsubsequent correlation analysis. Many modes of correlation analysisrequire the input data to be placed in “bins”. The speed of thecorrelation calculation is often determined by the total number of binspresent. Typical correlation analysis is performed “bin-to-bin”, meaningthat similar values contained in the same corresponding bin positiontend to make the correlation coefficient higher. It is thereforeimportant to make sure that the mass spectrometric data for eachspectrum is pre-processed, or “binned”, in a way that guarantees thatcommon ions between correlated spectra are placed in the samecorresponding bin position. For purposes of the present invention, a binposition corresponds to the mass portion of a given ion data pair, andthe value placed in the bin corresponds to the intensity portion.

The following steps are performed to convert each mass/intensity pair ina given spectrum, thus producing the pre-processed, or “binned”, form ofthat spectrum:

1. If needed, convert all mass spectra acquired in profile mode tocentroid mode. This is standard practice in the field, and is typicallydone using the software included by the instrument vendor, resulting ina single mass/intensity pair for each ion component observed in a massspectrum. All examples in this disclosure assume that all mass spectrawere acquired in centroid mode or converted thereafter.

2. Reduce the precision of each mass value to some user-definedprecision value. This can be done using one of many differentapproaches. For all examples shown in this disclosure, each mass valuewas simply truncated to produce the integer form, which corresponds to aprecision value of 0. However, more elaborate approaches can be usedwhich take into account theoretical decimal contributions based onchemical composition trends which occur relative to mass. These moreelaborate approaches typically have the net effect of providing adecision point either to round up or round down a mass value based onits overall mass, assumed chemical composition, and desired precisionvalue. Also of note, it is desirable for the user of the presentinvention to apply the same approach to shifts or perturbations thatrefer to known chemical modifications being considered in the algorithm.This is ultimately left up to the user, since the list of shifts orperturbations is user-defined and fully customizable. For the examplesshown in this disclosure, all included shifts or perturbationscorresponding to known chemical modifications were determined based on aprecision value of 0.

3. Relate each converted mass value to a particular bin position/number.The bin position for any given converted mass value is typically(10^precision value)×(converted mass value). For all examples shown inthis disclosure, the converted integer mass value itself determines thebin number, since a precision value of 0 was used. For example,10^0×123=123. As another example, a precision value of 1 and convertedmass value of 123.4 would correspond to a bin position of 1234.

4. Normalize all intensity values in a given spectrum based on a rangebetween 0 and 1. Basically, this step is done by dividing all intensityvalues in a given mass spectrum by the maximum intensity value in thatset.

5. Place the normalized intensity of each mass/intensity data pair inthe corresponding bin position, as determined in Step 3, above.

6. For any two pre-processed spectra to be correlated, normalize eachspectrum so that they have the same number of bins. This normalizationis distinct from the normalization of intensities discussed above. Forall examples shown in this disclosure, the number of bins betweencorrelated pre-processed spectra are normalized by zero-filling thespectrum containing fewer bins up to the same number of bins containedin the larger spectrum. For example, if pre-processed spectrum 1 has atotal of 400 bins, i.e. the highest normalized intensity corresponds toa bin position of 400, and spectrum 2 has a total of 500 bins, 100 binscontaining a value of 0 are added to the end of spectrum 1, thuscreating two pre-processed spectra with the same number of total bins.

All references, in this disclosure, to ion, mass, unknown spectrum, orknown spectrum, are assumed to refer to the pre-processed forms asdescribed above.

Subject to the above limitations, one supplies to the program a list ofknown ions, a list of unknown ions, and a list of shifts.

The method as described above is then performed, by producing a set ofall possible combinations of selected unknown ions, as modified by anyor all of the shifts on the list. The modifications are preferably madeby subtracting the shifts, as described above.

In a preferred embodiment, the program includes logic that preventsspecific shift combinations from being made, based on mathematical andapplication considerations.

Finally, the perturbed or modified spectra are correlated with the knownspectrum. For each correlation, the system stores the modified ions andthe shifts used, and the corresponding correlation coefficient.

After the correlation scores are obtained, it is necessary to choose a“best” perturbed spectrum, i.e. a spectrum being most closely related tothe known spectrum. Knowledge of the best spectrum helps to show how theunknown spectrum is similar to, and how it is different from, the knowncompound, so as ultimately to provide information on molecularstructure.

Most of the information derived from the “best” perturbed spectrum comesfrom how the ions were shifted to produce an enhanced correlation. Forexample, ion 300 may have been shifted by −16, to place the ion at 284,causing the ion to become aligned with a 284 ion in the known spectrum,causing the correlation coefficient to become very high. This resultmight indicate, in the field of metabolism, for example, thehydroxylation of substructure 284 of the known compound. Informationalso comes from ions that were not shifted, especially ions common toboth the unknown and the known or reference spectra.

The example given in FIGS. 1 and 2 provides further illustration of theabove principles. The “best” perturbed spectrum, shown in FIG. 2,represents the “best” combination of shifted ions, and the figure showsthe corresponding shifts in parentheses. If one were to apply thelabeled shifts to the corresponding ions (i.e. by removing the shifts),the result is a perturbed unknown spectrum having a high correlationwith the known spectrum (FIG. 1), after all possible combinations ofions and shifts have been evaluated. Application logic is also used toremove perturbation results containing combinations that are unlikelyfor a particular application area. For the unknown spectrum, this “best”combination is:

163.7 (zero shift) 196.4 (+16 shift) 238.1 (+16 shift) 267.3 (zeroshift) 281.5 (+16 shift) 293.4 (zero shift) 418.1 (+32 shift)

The above result could be interpreted to indicate that the knowncompound is being hydroxylated (i.e. a shift of +16) at two differentlocations, with ions 196, 238, and 281 showing individual hydroxylationsand ion 418 showing both.

The above example illustrates the usefulness of the present invention inderiving information about the molecular structure of an unknownsubstance, based on correlation with a spectrum of a known or referencesubstance, and possibly based on knowledge of the molecular structure ofthe known or reference substance.

FIGS. 3–8 illustrate an application of the method described above. Theillustrated application involves the use of the procedure known in theart as LC/MS (liquid chromatography/mass spectrometry). In a liquidchromatograph, components are separated by the LC device over a periodof time, and introduced into the mass spectrometer for collection ofmass spectra at distinct time intervals. The result is a collection orseries of mass spectra, taken over a period of time, in normal modeand/or MS/MS mode, corresponding to the separated components from the LCdevice.

The methodology of the present invention can be used to find, and removefrom further consideration, common components appearing in two distinctsamples analyzed by LC/MS. In the field of LC/MS, this is often called“background subtraction”. The present invention is applied in thismanner by correlating MS/MS spectra from the two analyzed samples overtime.

For example, LC/MS can be applied to two samples containing common andunique components. Typically, one of the samples, the “known” or “blank”sample, contains components either of known origin or of no interest.The second sample, called the “unknown” or “assayed” sample, may containthe same known components as well as unique components of interest.Often the same LC method is used to separate both samples physically forcomparative purposes. Common components will separate in a similarmanner over time. Components common to both the known and unknownsamples are found by using the methodology of the present invention tocorrelate unperturbed MS/MS spectra from both samples over time.

In practicing the technique of background subtraction, as applied usingthe present invention, one first obtains all MS/MS spectra from theknown MS chromatogram within a user-defined time window, centered aroundthe current unknown MS/MS time, such that the parent mass (also known inthe art as the precursor ion) is the same as the parent mass (orprecursor ion) of the unknown MS/MS spectrum. If there are no MS/MSspectra present in the time window of interest, having the same parentmass as in the unknown MS/MS spectrum, the current unknown spectrum isconsidered distinct from the known spectra, and is considered ofsufficient interest to warrant further testing.

In addition to the above criterion, a decision may be made to retain anunknown spectrum, for further analysis, based on the followingconsiderations. A common component is defined by the presence of one ormore highly correlated MS/MS spectra present in both samples, in thesame user-defined time window. Two spectra are considered highlycorrelated, for purposes of this invention, by having an unperturbedcorrelation value which is above some user-defined level. MS/MS spectrain the unknown sample that are not highly correlated with any MS/MSspectra in the known sample, in the same user-defined time window, areconsidered unique to the unknown sample. Also, a common component canstill be considered unique to the unknown sample if the correspondingnormal mode or MS/MS mode signal between the known and unknown sample ishigher than a user-defined difference level. This process is repeatedfor each MS/MS spectrum in the unknown sample. Components consideredunique to the unknown sample are selected for further consideration,whereby their normal mode and/or MS/MS mode spectra are correlated tothe known mass spectrum using the perturbation approach previouslydescribed. More precise logic for determining when to select a spectrumfor further consideration is given below.

FIGS. 3–8 show an example. FIGS. 3 and 4 plot intensity (number ofcounts) against time, and are essentially two-dimensionalrepresentations of three-dimensional sets of data. The horizontal axisindicates the time at which a given normal or MS/MS mode spectrum wasobtained. There is thus a separate spectrum for each point in time. Thevertical axis represents the total summed signal (the sum of the counts)present in a given MS spectrum at each given time. Collectively, thisplot is called a Total Ion Chromatogram. These data contain both normaland MS/MS mode spectra; for the sake of simplicity, only the normal modeMS spectra were used to represent the chromatograms. FIG. 3 pertains tomeasurements taken on a known sample, and FIG. 4 represents measurementstaken on an unknown sample.

In FIG. 3, the vertical band, centered at the time of about 7.8 minutes,represents a user-defined range of times for which spectra will be takenfor comparison with the unknown sample. In FIG. 4, the thin verticalbar, also located at the time of about 7.8 minutes, represents the timeat which an unknown spectrum is taken. The unknown spectrum, at the timeindicated by the vertical line, is to be compared with various spectraof the known sample, taken at various times within the user-definedrange.

FIGS. 5–7 represent three known spectra, all taken within theuser-defined range of time represented in FIG. 3. FIG. 8 represents theunknown spectrum, taken at the time represented by the vertical line inFIG. 4. In this simplified example, the spectra of FIGS. 5–7 are to becompared with that of FIG. 8.

One then correlates each known spectrum, in the chosen time interval, asrepresented by FIGS. 5–7, with the unknown spectrum corresponding to theselected point in time (FIG. 8). One also calculates the total signal(the sum of all counts in the graph) for each spectrum, and one alsocalculates the ratio of the total signal for the unknown spectrum, tothe total signal for each of the known spectra. This ratio is called thetotal signal ratio.

The logic for determining when a candidate unknown spectrum being testedis considered unique, and worthy of further consideration, is asfollows. One selects a candidate unknown spectrum for furtherconsideration if any of the following criteria are satisfied:

a) all of the correlations between the candidate unknown spectrum andthe spectra in the known range are below a user-defined threshold; or

b) any of the correlations between the candidate unknown spectrum andthe spectra in the known range are above a user-defined threshold, andthe corresponding signal ratio is above a user-defined threshold; or

c) there are no MS/MS spectra in the tested time window having the sameparent mass (precursor ion) as in the unknown MS/MS spectrum.

Conversely, the candidate unknown spectrum being tested is considered abackground component, and not selected for further analysis by thepresent invention, if the following criteria are satisfied:

a) the comparison of the candidate unknown spectrum with known spectrain the selected time interval does not result in any combination of ahigh correlation and high signal ratio; and

b) there is at least one occurrence of both a high correlation value anda low signal ratio in the set of tested spectra.

For example, a given spectrum is therefore considered “background”, andnot worthy of further analysis, if it has a high correlation with one ofthe known spectra, and a low signal ratio, meaning that each samplecontains the same component at that given time, and that component isnot significantly more abundant in the unknown sample. On the otherhand, the spectrum is of interest if it has a high correlation with oneof the known spectra, and a high signal ratio, because the high signalratio indicates that the unknown sample contains significantly greateramounts of the common component at that time, and therefore may be ofinterest. If all of the correlations between the unknown spectrum andthe known spectra are low, the candidate unknown spectrum is retainedfor further analysis, again because it is different from all of theknown spectra.

In the example shown, the hypothetical correlations and signal ratiosare as shown in the following Table 3:

TABLE 3 User-Defined User-Defined Known Correlation Signal RatioSpectrum Correlation Threshold Signal Ratio Threshold 1 0.03 0.8 75 4.02 0.98 0.8 1.8 4.0 3 0.02 0.8 4.5 4.0

In the above example, the candidate unknown spectrum is considered abackground component, and would not be selected for further analysis bythe present invention. Spectrum 2 in the known spectrum range has acorrelation value above the user-defined threshold, and a signal ratiobelow the user-defined threshold. The latter indicates that thecomponents in the candidate unknown spectrum are similar to what is inthe known spectrum, and is therefore not sufficiently distinct towarrant further scrutiny. Spectra 1 and 3 have low correlation valuesand high signal ratios. Thus, according to the logic set forth above,the candidate unknown spectrum would be rejected. Note that, in thisexample, it has been assumed that the unknown spectrum had beensubjected to the initial test given above, i.e. there were some cases inwhich the known MS/MS spectra included a parent mass which was the sameas a parent mass in the unknown spectrum.

Although the above example was given with respect to liquidchromatography, the methodology described applies to any situation inwhich a series of mass spectra is obtained over a period of time.

FIG. 9 shows a block diagram of the system of the present invention.Mass spectrometer 101 is connected to programmed computer 102. Thecomputer 102 comprises the means for deriving the set of perturbedspectra, the means for comparing the spectra with the spectrum of areference substance, and the means for choosing a best member of the setof spectra. The computer 102 also comprises means for derivinginformation on the molecular structure of the unknown substance, and forselecting spectra from a time series of spectra.

The invention is not limited by the specific technique of correlation.Any method which compares two spectra, i.e. two graphs or sets ofordered pairs, and which provides a scalar number representing the“relatedness” of the two spectra, can be used in the present invention.Thus, for example, instead of using cross-correlation, one could performa least-squares analysis, or a Fourier analysis, or some other method ofcurve-fitting, or some other equivalent form of analysis, to make thecomparison.

In the sample mass spectra shown in the figures, there is a horizontalline, above the horizontal axis, which represents a user-definedthreshold. Signals below this threshold are normally ignored as they arepresumed to be artifacts, and not of interest. While the use of such athreshold is preferred, the invention can be practiced without it.Moreover, the position of the threshold, if used, can be varied.

All of the above alternatives should be considered within the spirit andscope of the following claims.

1. A method of analyzing data from a mass spectrometer, comprising: a)generating a first mass spectrum corresponding to a known sample and asecond mass spectrum corresponding to an unknown sample, b) applying aplurality of perturbations to said second spectrum so as to derive aplurality of perturbed spectra, c) comparing each of said plurality ofperturbed spectra to said first mass spectrum, and d) choosing one ofsaid perturbed spectra according to its correlation with said first massspectrum, wherein the generating step includes generating a plurality offirst and second mass spectra during a period of time, and wherein asecond mass spectrum used in step (b) is selected by comparing candidatesecond mass spectra with a plurality of first mass spectra, andselecting a candidate second mass spectrum which contains componentswhich are not in common with components in said first mass spectra.
 2. Amethod of analyzing data from a mass spectrometer, comprising: a)generating a first mass spectrum corresponding to a known sample and asecond mass spectrum corresponding to an unknown sample, wherein thefirst mass spectrum comprises a graph having a horizontal axisrepresenting masses of components in the known sample, and a verticalaxis representing intensity of each component, wherein the second massspectrum comprises a graph having a horizontal axis representing massesof components in the unknown sample, and a vertical axis representingintensity of each component, b) applying a plurality of perturbations tosaid second spectrum so as to derive a plurality of perturbed spectra,wherein each perturbation comprises a shift, along the horizontal axis,of a position of one of said components, c) comparing each of saidplurality of perturbed spectra to said first mass spectrum, and d)choosing one of said perturbed spectra according to its correlation withsaid first mass spectrum, wherein the generating step includesgenerating a plurality of first and second mass spectra during a periodof time, and wherein a second mass spectrum used in step (b) is selectedby comparing candidate second mass spectra with a plurality of firstmass spectra, and selecting a candidate second mass spectrum whichcontains components which are not in common with components in saidfirst mass spectra.